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Assessing spatial accessibility to primary health care services has become increasingly important since recent research has demonstrated that poor access to primary health care services contribute to lower health care utilization, which may in turn give rise to poorer health outcomes. Health care practitioners are increasingly relying on accessibility analysis and modelling in order to identify health care shortage areas and efficiently allocate human and financial resources to those needy areas in order to alleviate health care accessibility problems. This presentation demonstrates how the use of big data and geographical information system can increase the sophistication of traditional accessibility analysis and thus support health care practitioners in delivering more smart and sustainable health care strategies. In particular, this talk addresses two often-neglected issues in current accessibility analysis: the use of public transport as travel mode to reach health care facilities and ii) the daily fluctuations in health care accessibility that may occur over the day due to variations in service provision and/or variation in infrastructure performances. The presentation illustrates some preliminary results of an ongoing research project developed by the University Complutense of Madrid (Spain) and the University of Naples Federico II (Italy) aimed at assessing temporal variability in transit based-accessibility to primary health care services in the Metropolitan Area of Madrid. The project relies on the efficient calculation of travel time between a large numbers of origin-destination pairs, repeated for many departure times. It integrates publicly available digital data such as General Transit Feed Specification data from the Madrid Region Transit Authority, pedestrian network data from OpenStreetMap and health care services data from the Madrid Health Portal with custom ArcGIS tools in order to provide a complete and realistic picture of health care service accessibility in the Metropolitan Area of Madrid. Daily fluctuation in transit-based accessibility to primary health care services are also analyzed using easily interpretable summary statistics such as average and standard deviation of accessibility across predefined time periods. The results of the analysis are compared to those obtained when using traditional accessibility models, indicating important differences in spatial accessibility patterns. The transferability of the model is illustrated through a second application in the urban area of Milan, Italy. The presentation concludes by drawing attention to the potential benefits for including more intelligent modelling inputs and by reiterating the policy importance of developing more accurate evaluations of the current and future state of the public transport network and health care delivery system in the Metropolitan Area of Madrid.